Integrating Multi-Engine OCR and Retrieval-Augmented Generation for Historical Document Understanding

Historical document digitization remains a challenging task due to document degradation, multilingual scripts, complex layouts, and inconsistent writing styles, which significantly affect the performance of conventional Optical Character Recognition (OCR) systems. Existing approaches often rely on a single OCR engine and lack contextual understanding, resulting in poor recognition accuracy and limited adaptability to diverse historical collections. This paper presents RAG-OCR, a Retrieval-Augmented Generation (RAG)-based hybrid OCR framework for robust recognition of historical documents with complex layouts. The proposed framework integrates image preprocessing, document layout analysis, and an ensemble of OCR engines to extract textual content from heterogeneous document regions. Retrieved contextual information from domain-specific knowledge repositories is combined with Large Language Models (LLMs) to perform intelligent post-OCR correction, resolve ambiguities, and restore semantically coherent text. The framework further incorporates confidence-based fusion and context-aware refinement to enhance recognition accuracy while preserving the structural integrity of historical documents. By leveraging retrieval-augmented reasoning and hybrid OCR, the proposed approach reduces recognition errors caused by degraded text, noise, and script variability, while improving scalability across multilingual and unstructured document collections. The proposed framework offers a practical and intelligent solution for digital preservation, archival management, cultural heritage conservation, and historical document analysis.